Decision maker based on nanoscale photo-excitation transfer.
Ontology highlight
ABSTRACT:
SUBMITTER: Kim SJ
PROVIDER: S-EPMC3738946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Decision making is critical in our daily lives and for society in general and is finding evermore practical applications in information and communication technologies. Herein, we demonstrate experimentally that single photons can be used to make decisions in uncertain, dynamically changing environments. Using a nitrogen-vacancy in a nanodiamond as a single-photon source, we demonstrate the decision-making capability by solving the multi-armed bandit problem. This capability is directly and immediately associated with single-photon detection in the proposed architecture, leading to adequate and adaptive autonomous decision making. This study makes it possible to create systems that benefit from the quantum nature of light to perform practical and vital intelligent functions.
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Project description:This study describes a method to assist the task of predicting the result of the decision-making process of an individual based on psychological and emotional aspects and using artificial intelligence (AI) techniques. This study presents indicators created for profile identification, which are organized in primary and circumstantial categories. These indicators are merged according to the ultimate purpose of profile identification, including the expected behavioral pattern for a person who performs a decision-making process. The person behavior hypothesis was successfully tested and can be approximated by an indicator such as mental functioning pattern, and the mental functioning pattern hypothesis can signal the most likely decisions of an individual. Four debtor decision variables were assessed in a debt negotiation process, in order to validate the method, which is applicable to other decision-making domains. The best signaling of the most likely decision of the debtor was seven times greater than that of a random prediction, while the gain of the worst decision signaling variable was 20%.
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Project description:Decision-making is being performed frequently in areas of computation to obtain better performance in a wide variety of current intelligent activities. In practical terms, this decision-making must adapt to dynamic changes in environmental conditions. However, because of limited computational resources, adaptive decision-making is generally difficult to achieve using conventional computers. The ionic decision-maker reported here, which uses electrochemical phenomena, has excellent dynamic adaptabilities, as demonstrated by its ability to solve multiarmed bandit problems (MBPs) in which a gambler given a choice of slot machines must select the appropriate machines to play so as to maximize the total reward in a series of trials. Furthermore, our ionic decision-maker successfully solves dynamic competitive MBPs, which cause serious loss due to the collision of selfish users in communication networks. The technique used in our devices offers a shift toward decision-making using the motion of ions, an approach that could find myriad applications in computer science and technology, including artificial intelligence.
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Project description:BackgroundAlmost 20 million children under one year of age did not receive basic vaccines in 2019, and most of these children lived in low- and middle-income countries. Implementation research has been recognized as an emerging area that is critical to strengthen the implementation of interventions proven to be effective. As a component of strengthening implementation, WHO has called for greater embedding of research within decision-making processes. One strategy to facilitate the embedding of research is to engage decision-makers as Principal Investigators of the research. Since 2015, the Alliance for Health Policy and Systems Research within the WHO and the United Nations Children's Fund have supported decision-maker led research by partnering with Gavi, the Vaccine Alliance, in an initiative called "Decision-Maker Led Implementation Research". This synthesis paper describes the cross-cutting lessons from the initiative to further understand and develop future use of the decision-maker led strategy.MethodsThis study used qualitative methods of data collection, including a document review and in-depth interviews with decision-makers and researchers engaged in the initiative. Document extraction and thematic content analysis were applied. The individual project was the unit of analysis and the results were summarized across projects.ResultsResearch teams from 11 of the 14 projects participated in this study, for an overall response rate of 78.6%. Most projects were carried out in countries in Africa and conducted at the sub-state or sub-district level. Seven enablers and five barriers to the process of conducting the studies or bringing about changes were identified. Key enablers were the relevance, acceptability, and integration of the research, while key barriers included unclear results, limited planning and support, and the limited role of a single study in informing changes to strengthen implementation.ConclusionsDecision-maker led research is a promising strategy to facilitate the embedding of research into decision-making processes and contribute to greater use of research to strengthen implementation of proven-effective interventions, such as immunization. We identified several lessons for consideration in the future design and use of the decision-maker led strategy.
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Project description:BackgroundThe use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).MethodsWe conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.ResultsOf 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.ConclusionsA small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.
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Project description:BackgroundComputerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit.MethodsWe conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.ResultsSixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements.ConclusionsCCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.
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Project description:Efficient and accurate decision making is gaining increased importance with the rapid expansion of information communication technologies including artificial intelligence. Here, we propose and experimentally demonstrate an on-chip, integrated photonic decision maker based on a ring laser. The ring laser exhibits spontaneous switching between clockwise and counter-clockwise oscillatory dynamics; we utilize such nature to solve a multi-armed bandit problem. The spontaneous switching dynamics provides efficient exploration to find the accurate decision. On-line decision making is experimentally demonstrated including autonomous adaptation to an uncertain environment. This study paves the way for directly utilizing the fluctuating physics inherent in ring lasers, or integrated photonics technologies in general, for achieving or accelerating intelligent functionality.
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